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International Journal of Crowd Science 2019, 3(3): 249-266 https://doi.org/10.1108/IJCS-05-2019-0014
Research paper | Open Access | Issue | Published: 14 October 2019

Intelligence level analysis for crowd networks based on business entropy

Show Author's Information Zhouxia Li1,  Zhiwen Pan2,  Xiaoni Wang1,  Wen Ji3,  Feng Yang1( )
Beijing Jiaotong University, Beijing, China
Department of Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing, China
Institute of Computing Technology Chinese Academy of Sciences, Beijing, China
Keywords:
Differential evolution, Crowd intelligence, Business entropy, Network profession distribution, Subject interaction patterns
Cite this article:
Li Z, Pan Z, Wang X, et al. Intelligence level analysis for crowd networks based on business entropy. International Journal of Crowd Science, 2019, 3(3): 249-266. https://doi.org/10.1108/IJCS-05-2019-0014
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Abstract Full text About this article
Purpose

Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Design/methodology/approach

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

Findings

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

Originality/value

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.


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About this article

Intelligence level analysis for crowd networks based on business entropy

Show Author's information Zhouxia Li1, Zhiwen Pan2, Xiaoni Wang1, Wen Ji3, Feng Yang1( )
Beijing Jiaotong University, Beijing, China
Department of Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing, China
Institute of Computing Technology Chinese Academy of Sciences, Beijing, China

Abstract

Purpose

Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Design/methodology/approach

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

Findings

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

Originality/value

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.

Keywords: Differential evolution, Crowd intelligence, Business entropy, Network profession distribution, Subject interaction patterns

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Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 28 May 2019
Accepted: 01 September 2019
Published: 14 October 2019
Issue date: December 2019

Copyright

© The author(s)

Acknowledgements

Acknowledgements

This work is supported by the National Key R&D Program of China (2017YFB1400100) and the National Basic Research Program of China under Grant No. 61671049, 61301002.

Rights and permissions

Zhouxia Li, Zhiwen Pan, Xiaoni Wang, Wen Ji and Feng Yang. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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